The A of Separation

252

167

In a recent paper, Traulsen and Nowak use a multilevel selection model to show that cooperation can be favored by group selection in finite populations [Traulsen A, Nowak M (2006) Proc Natl Acad Sci USA 103:10952-10955]. The authors challenge the view that kin selection may be an appropriate interpretation of their results and state that group selection is a distinctive process ''that permeates evolutionary processes from the emergence of the first cells to eusociality and the economics of nations.'' In this paper, we start by addressing Traulsen and Nowak's challenge and demonstrate that all their results can be obtained by an application of kin selection theory. We then extend Traulsen and Nowak's model to life history conditions that have been previously studied. This allows us to highlight the differences and similarities between Traulsen and Nowak's model and typical kin selection models and also to broaden the scope of their results. Our retrospective analyses of Traulsen and Nowak's model illustrate that it is possible to convert group selection models to kin selection models without disturbing the mathematics describing the net effect of selection on cooperation.T raulsen and Nowak (1) (T&N) present a multilevel selection model and demonstrate that a mutant helping allele can be favored to fixation, when introduced as a single copy in a population monomorphic for selfishness ifwhere c is the cost of helping, b the benefit of helping for group members (excluding the actor), N the group size, n g the number of groups, the migration rate between groups, and q the probability of group splitting (T&N, inequality ineq. 2). In their conclusion, T&N challenge the view that kin selection is an appropriate interpretation of their results and state that:''It would be interesting to see how the mathematical methods of kin selection can be used to derive our central results given by eqs. 1-3 and what assumption are needed for such a derivation. The problem is that typical methods of kin selection are based on traditional considerations of evolutionary stability, which are not decisive for games in finite populations.'' Further, in a recent comment on the various possible mechanisms leading to the evolution of cooperation, Nowak (2) states that the group selection model of T&N results in a different process than kin selection. These are surprising statements, given that many authors have emphasized that group selection models are not different from kin selection models (3-8), and that kin selection theory has been extended to finite populations that can follow very diverse demographic regimes (8)(9)(10)(11)(12)(13)(14). To us, the mechanism favoring cooperation in T&N's model is clearly kin selection. Indeed, kin selection operates whenever interactions occur among genetic relatives, that is, among individuals who tend to share a more recent common ancestor than individuals sampled randomly from the whole population. This may happen when interactions take place within families before the dispersal of offspring, or whe...

mentioning

confidence: 58%

Group selection and kin selection: Two concepts but one process

Lehmann

Keller

West

et al. 2007

252

167

“…At equilibrium, F ST , defined as the amount of genetic variation between subpopulations expressed as a fraction of the total variation (41), is given by the expression.…”

Section: Resultsmentioning

confidence: 99%

“…Genetic differentiation between subpopulations was quantified by using estimates of F ST , the fraction of the total genetic variation found between subpopulations (41) obtained from an analysis of molecular variance (AMOVA) in ARLEQUIN ver. 1.1 (42) assuming a Jukes-Cantor (43) model to correct for multiple hits.…”

Section: Methodsmentioning

confidence: 99%

Genetic drift and within-host metapopulation dynamics of HIV-1 infection

Frost

Dumaurier

Wain‐Hobson

et al. 2001

126

Most HIV replication occurs in solid lymphoid tissue, which has prominent architecture at the histological level, which separates groups of productively infected CD4 ؉ cells. Nevertheless, current population models of HIV assume panmixis within lymphoid tissue. We present a simple ''metapopulation'' model of HIV replication, where the population of infected cells is comprised of a large number of small populations, each of which is established by a few founder viruses and undergoes turnover. To test this model, we analyzed viral genetic variation of infected cell subpopulations within the spleen and demonstrated the action of founder effects as well as significant variation in the extent of genetic differentiation between subpopulations among patients. The combination of founder effects and subpopulation turnover can result in an effective population size much lower than the actual population size and may contribute to the importance of genetic drift in HIV evolution despite a large number of infected cells.H IV has an enormous evolutionary potential because of the combination of a high mutation rate (Ϸ3 ϫ 10 Ϫ5 substitutions͞site͞generation; ref. 1), a short generation time (Ϸ2.6 days; refs. 2 and 3), and a large number of infected cells within the host (10 7 -10 8 infected cells; ref. 4). Many analyses of the pattern of nucleotide substitution in the viral env gene have detected evolution in response to positive selection (5-16), although relatively few studies have linked these patterns of viral genetic variation to selection by defined immune responses or for changes in viral phenotype associated with coreceptor usage. In the presence of antiretroviral agents that target viral protease and reverse transcriptase, positive selection has been described in numerous studies, which demonstrate convergent evolution in different individuals of mutations that confer drug resistance (17)(18)(19)(20). Although specific mutations have been identified with escape from immune responses and escape from antiretrovirals, viral genetic variation reflects more than just these focused positive selection pressures and may reflect more subtle processes of within-host competition between viruses for higher replicative potential.Despite an enormous potential to respond to selection, there can be substantial differences between infected individuals in the evolution of drug resistance. For example, the frequency of single drug-resistant mutants in viral protease and reverse transcriptase before therapy can vary dramatically between hosts (21-23), apparently by random. Indirect observations of the apparently stochastic way in which multiply resistant mutants emerge during therapy are also consistent with a role of genetic drift (18,24,25). In addition, three studies of the evolution of env gene sequences have found substantially lower genetic variation than expected for a population of the order of 10 7 (24,26,27). These studies estimated the ''effective'' population size to be of the order of 10 3 , reflecting the high relatedness be...

“…BATs were then used to determine the source population of 50 genotypes. The resulting posterior probabilities were assessed as a function of F st (12), here estimated between pairs of populations with the Weir and Cockerham estimator, (13), and calculated with the program FSTAT (14). The strength of multilocus linkage associations was assessed with the standardized index of association (I A ; ref.…”

Section: [1]mentioning

confidence: 99%

Disease surveillance in recombining pathogens: Multilocus genotypes identify sources of human Coccidioides infections

Fisher

Rannala

Chaturvedi

et al. 2002

Molecular surveillance of pathogenic microbes works by genotyping isolates with DNA fingerprinting techniques and then using these genotypes to assign individuals to populations. Clonality is assumed in many fingerprinting studies, although this assumption has been shown to be false for many organisms. To accommodate recombining organisms into surveillance programs, methods using population allele frequencies in combination with individual multilocus genotypes are necessary. Here, we develop a statistical method appropriate for haploid recombining microbes that allows individuals to be assigned to populations. We illustrate the usefulness of this technique by inferring the source populations for Coccidioides isolates recovered from patients treated outside the endemic area of Coccidioides sp., the etiological agents of human coccidioidomycosis, but with a travel history including visits to one or more endemic areas.